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MiMo-V2.6 scales agentic reinforcement learning to 1M-token contexts

huggingface.co by Core Team et al. Editor score 8/10

MiMo-V2.6 reports asynchronous RL steps consuming 1,568 samples and 2.7–3.7B tokens at contexts up to 1M, with diverse agent environments, groupwise grading and a frozen MoE router. Its training and stability choices offer concrete material for teams scaling model RL.

What

MiMo-V2.6 is an omni-modal model family that scales reinforcement learning compute toward model self-improvement.

Method

Training combines contexts up to 1M tokens, diverse agent environments, groupwise grading, and defenses including a frozen MoE router.

Why it matters

Unified trajectories, high-concurrency rollouts, and decoupled control and data planes support mixed-task agentic RL at scale.

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